class_docstr: Class for the manifold of positive reals.
  
  The real positive axis endowed with the Information geometry metric.
class_docstr_tok: ["Class","for","the","manifold","of","positive","reals",".","The","real","positive","axis","endowed","with","the","Information","geometry","metric","."]
docstr: Sample in the positive reals.
  
  Parameters
  ----------
  n_samples : int
      Number of samples.
      Optional, default: 1.
  bound : float
      Bound of the interval in which to sample in the tangent space.
      Optional, default: 1.
  atol : float
      Tolerance.
      Optional, default: backend atol.
  
  Returns
  -------
  samples : array-like, shape=[..., 1]
      Points sampled in the positive reals.
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code: def random_point(n_samples=1, bound=1.0, atol=gs.atol):
      """Sample in the positive reals.
  
      Parameters
      ----------
      n_samples : int
          Number of samples.
          Optional, default: 1.
      bound : float
          Bound of the interval in which to sample in the tangent space.
          Optional, default: 1.
      atol : float
          Tolerance.
          Optional, default: backend atol.
  
      Returns
      -------
      samples : array-like, shape=[..., 1]
          Points sampled in the positive reals.
      """
      size = (n_samples, 1) if n_samples != 1 else (1,)
      return (bound - atol) * gs.random.rand(*size) + atol
  
file_path: geomstats/geomstats/geometry/positive_reals.py
imports: import geomstats.backend
  from geomstats.geometry.base import VectorSpaceOpenSet
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.geometry.riemannian_metric import RiemannianMetric
label_desc: Use image node_id 5 for calling the PositiveReals obj's underlying member method code with example usage: obj.random_point(n_samples, bound, atol) and returns: unknown